Abstract
The rapid growth in the volume, variety, and velocity of geospatial data has created data ecosystems that are highly distributed, heterogeneous, and semantically inconsistent. Existing data catalogs, portals, and infrastructures still rely largely on keyword-based search with limited semantic support, which often fails to capture user intent and leads to weak retrieval performance. To address these challenges, this study proposes a knowledge graph-driven multi-agent framework for intelligent geospatial data discovery, powered by large language models. The framework introduces a unified geospatial metadata ontology as a semantic mediation layer to align heterogeneous metadata standards across platforms and constructs a geospatial metadata knowledge graph to explicitly model datasets and their multidimensional relationships. Building on the structured representation, the framework adopts a multi-agent collaborative architecture to perform intent parsing, knowledge graph retrieval, and answer synthesis, forming an interpretable and closed-loop discovery process from user queries to results. Results from representative use cases and performance evaluation show that the framework substantially improves intent matching accuracy, ranking quality, recall, and discovery transparency compared with traditional systems. This study advances geospatial data discovery toward a more semantic, intent-aware, and intelligent paradigm, providing a practical foundation for next-generation intelligent and autonomous spatial data infrastructures and contributing to the broader vision of Autonomous GIS.
Abstract (translated)
地理空间数据在体量、种类和速度上的快速增长，催生了高度分布式、异构且语义不一致的数据生态系统。现有数据目录、门户和基础设施仍主要依赖基于关键词的搜索，语义支持有限，往往难以捕捉用户意图，导致检索性能薄弱。为应对这些挑战，本研究提出一种由知识图谱驱动、基于大语言模型的多智能体智能地理空间数据发现框架。该框架引入统一的地理空间元数据本体作为语义中介层，以对齐跨平台的异构元数据标准，并构建地理空间元数据知识图谱，显式建模数据集及其多维关系。基于这一结构化表示，框架采用多智能体协同架构执行意图解析、知识图谱检索与答案合成，形成从用户查询到结果的可解释闭环发现流程。代表性用例与性能评估结果表明，相较于传统系统，该框架在意图匹配准确率、排序质量、召回率及发现透明度方面均有显著提升。本研究推动地理空间数据发现迈向更语义化、意图感知和智能化的范式，为下一代智能自主空间数据基础设施提供了实践基础，并贡献于自主GIS的宏观愿景。
URL
https://arxiv.org/abs/2603.20670
